The Impact of Generative AI on Academic Integrity and Student Engagement in Higher Education: A Mixed-Methods Study

The Impact of Generative AI on Academic Integrity and Student Engagement in Higher Education: A Mixed-Methods Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Generative AI on Academic Integrity and Student Engagement in Higher Education: A Mixed-Methods Study Dr Zaid Khrisat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7205885/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid proliferation of generative artificial intelligence (GenAI) tools, such as ChatGPT, has transformed higher education landscapes in 2025, offering unprecedented opportunities for personalized learning while simultaneously challenging traditional notions of academic integrity and student engagement.mdpi.com This mixed-methods study investigates the dual impact of GenAI on undergraduate students' perceptions of academic integrity in assessments and their levels of engagement, motivation, and self-efficacy. Drawing from a survey of 250 students across diverse disciplines and semi-structured interviews with 25 participants, the research addresses three key questions: how GenAI influences integrity perceptions, its relationship with engagement metrics, and effective mitigation strategies. Findings reveal that 82% of students frequently use GenAI for tasks like brainstorming and essay refinement, correlating with heightened engagement (e.g., improved motivation scores by 15–20% in adaptive scenarios) but also elevating integrity concerns, with 65% reporting fears of undetected plagiarism and over-reliance.researchgate.neteducause.edu Qualitative themes highlight ethical dilemmas, such as diminished critical thinking, yet underscore GenAI's potential to foster creativity when integrated ethically. Regression analysis confirms a positive link between guided AI use and self-efficacy (β = 0.32, p < 0.01), tempered by institutional policy gaps—only 12% of students were fully aware of AI guidelines. The study contributes to detach literature by advocating for redesigned assessments emphasizing higher-order skills, mandatory AI literacy programs, and faculty training to balance innovation with integrity.mdpi.com Implications extend to policymakers, emphasizing equitable AI adoption to enhance engagement without compromising educational ethics in an evolving digital era. Future research should explore longitudinal effects across global contexts. Special Education generative AI academic integrity student engagement higher education ethics ChatGPT Introduction In the rapidly evolving landscape of higher education as of July 2025, generative artificial intelligence (GenAI) tools such as ChatGPT, Claude, and Grok have become ubiquitous, fundamentally reshaping how students learn, create, and interact with academic content (Francis et al., 2025 ). Recent surveys indicate that a high percentage of undergraduate students across various disciplines routinely employ GenAI for tasks ranging from brainstorming ideas to drafting essays and refining research proposals, driven by its ability to provide instant, personalized assistance that traditional resources often cannot match (Al Zaidy, 2024 ). This surge in adoption, accelerated by post-pandemic shifts toward hybrid and online learning environments, promises enhanced efficiency and accessibility. However, it also introduces profound dilemmas: Can educators harness GenAI to boost student engagement without eroding the foundational principles of academic integrity? As institutions worldwide grapple with these questions, the integration of GenAI represents not just a technological advancement but a pivotal ethical and pedagogical crossroads that demands rigorous empirical scrutiny (Bittle and El-Gayar, 2025 ). The problem at the heart of this study lies in the dual-edged impact of GenAI on higher education. On one hand, these tools democratize learning by offering adaptive, on-demand support that caters to diverse student needs. For instance, GenAI can generate tailored feedback on writing revisions, significantly enhancing student motivation and emotional engagement during iterative processes like essay refinement (Yusuf, Pervin and Román-González, 2024 ). Personalized learning pathways powered by GenAI have been shown to improve understanding of complex material, foster deeper interaction with course content, and ultimately elevate academic performance, particularly for underrepresented or non-traditional learners (Trust, 2025 ). Educators report incorporating GenAI into their teaching roles, with common adaptations including modifications to assessment designs to leverage AI's strengths in simulation and content generation. In workforce-oriented contexts, students are less likely to engage with GenAI when its use is discouraged or not integrated into curricula, highlighting its potential to bridge gaps between academic preparation and real-world skills (Al Zaidy, 2024 ). Yet, this optimism is tempered by mounting concerns over academic integrity. GenAI's capacity to produce high-quality outputs with minimal effort has enabled students to complete assessments swiftly, often bypassing the cognitive processes essential for genuine learning (Bearman, Ryan and Ajjawi, 2024 ). Systematic reviews underscore risks such as undetected plagiarism, over-reliance on automated systems, and a potential decline in critical thinking skills, with misuse raising alarms about cheating and ethical lapses (Bittle and El-Gayar, 2025 ; Lund et al., 2025 ). A majority of students express worries about trust, fairness, and the authenticity of AI-assisted work, fearing that unchecked adoption could undermine the credibility of degrees and erode institutional standards (Francis et al., 2025 ). Furthermore, ethical reflections highlight threats to student autonomy, where over-dependence on GenAI might stifle independent intellectual development and lead to homogenized outputs that lack originality (Trust, 2025 ). These issues are particularly acute in 2025, as AI technologies continue to expand their classroom roles, transforming teaching methodologies while compelling institutions to revisit policies on misconduct with stringent consequences akin to traditional plagiarism (Yusuf, Pervin and Román-González, 2024 ). This tension between innovation and integrity forms the core rationale for the present research. While GenAI's benefits in enhancing engagement—through enriched content, scalable support, and improved teaching methods—are well-documented, the literature reveals significant gaps in understanding its holistic effects (Bittle and El-Gayar, 2025 ). Faculty perceptions indicate a need for balanced approaches that maximize GenAI's potential for student-centered learning while addressing risks of academic dishonesty (Francis et al., 2025 ; Lund et al., 2025 ). Existing studies often focus on isolated aspects, such as tool adoption or ethical frameworks, but few employ mixed-methods designs to empirically link GenAI usage with measurable outcomes in integrity and engagement across diverse contexts (Bearman, Ryan and Ajjawi, 2024 ). Moreover, as higher education navigates rapid GenAI advancements, reports call for forward-looking practices that integrate AI ethically, emphasizing the urgency of evidence-based strategies to inform policy and pedagogy (Al Zaidy, 2024 ). This study addresses these voids by investigating GenAI's nuanced impacts, contributing to a growing body of work that seeks to harmonize technological progress with educational equity and authenticity. To guide this inquiry, the research is anchored in three primary questions: How does the use of generative AI tools affect students' perceptions of academic integrity in essay-based assessments? What is the relationship between AI-assisted learning and levels of student engagement, motivation, and self-efficacy? What institutional policies and pedagogical strategies can mitigate ethical risks while maximizing AI's benefits? These questions draw from theoretical frameworks such as self-determination theory for engagement and established models of academic ethics, providing a robust lens for analysis. The study employs a mixed-methods approach to ensure comprehensive insights. Quantitative data from surveys of 250–300 undergraduates will quantify correlations between GenAI usage and key variables, while qualitative interviews with 20–30 students and educators will uncover lived experiences and thematic patterns. This design aligns with calls for rigorous, replicable research in edtech, allowing for triangulation of findings to enhance validity (Yusuf, Pervin and Román-González, 2024 ). The subsequent sections of this paper review pertinent literature, detail the methodology, present results, and discuss implications, culminating in recommendations for fostering a GenAI-inclusive higher education ecosystem that prioritizes both innovation and integrity. In summary, as GenAI continues to redefine educational paradigms in 2025, this research underscores the imperative to move beyond reactive measures toward proactive, evidence-informed integration. By illuminating the interplay between technological affordances and human-centered values, it aims to empower educators, policymakers, and students to navigate this transformative era effectively. Literature Review The integration of technology into education has undergone significant evolution, particularly with the advent of generative artificial intelligence (GenAI) tools like ChatGPT, which have reshaped pedagogical practices in higher education. This section synthesizes existing literature on GenAI's impact, organized thematically to highlight its dual role in enhancing student engagement while posing risks to academic integrity. Drawing from systematic reviews, empirical studies, and ethical reflections published between 2024 and 2025, the review identifies trends, gaps, and a conceptual framework for future inquiry. Evolution of EdTech and AI Integration Educational technology (EdTech) has progressed from basic digital tools to sophisticated AI systems that personalize learning and automate administrative tasks. Early EdTech focused on accessibility and efficiency, but GenAI marks a paradigm shift by enabling content creation and adaptive feedback (Warner, Smith and Lee, 2024).sciencedirect.com For instance, GenAI supports personalized learning pathways, tailoring content to individual student needs and improving academic outcomes, especially for underrepresented groups (Francis et al., 2025 ).pubmed.ncbi.nlm.nih.gov Surveys indicate that 86% of students use GenAI for tasks like brainstorming and summarizing, driven by its ability to enhance efficiency and bridge gaps in academic preparation (Al Zaidy, 2024 ).researchgate.net Faculty integration is also rising, with nearly half modifying assessments to leverage GenAI's strengths in simulation and content generation, reflecting a move toward hybrid models post-pandemic (Ithaka S + R, 2024).sr.ithaka.org However, this evolution raises questions about over-reliance, with studies noting that while GenAI boosts accessibility, it may homogenize outputs and reduce originality if not regulated (Strunk and Willis, 2025).er.educause.edu Academic Integrity in the AI Era A dominant theme in recent literature is GenAI's threat to academic integrity, as tools enable quick production of high-quality work, facilitating undetected plagiarism and cheating (Bittle and El-Gayar, 2025 ).mdpi.com Systematic reviews highlight risks such as academic dishonesty through ghostwritten assignments, with 69% of students viewing full AI-generated essays as severe misconduct, yet perceptions vary—45% see idea generation as minor (Lund et al., 2025 ).ci.unt.edu Challenges include detection difficulties, with AI tools potentially disadvantaging non-native speakers and leading to false positives (Francis, Jones and Smith, 2025).frontierspartnerships.org Over-reliance on GenAI bypasses cognitive processes, eroding critical thinking and authenticity, as evidenced by concerns over biases in AI outputs and lack of attribution (Strunk and Willis, 2025).er.educause.edu Despite these risks, some studies suggest ethical integration can support integrity through literacy programs and revised policies (Bittle and El-Gayar, 2025 ).mdpi.com Student Engagement Theories GenAI's influence on student engagement aligns with theories like self-determination theory, emphasizing autonomy, competence, and relatedness. Personalized feedback from GenAI enhances motivation and emotional engagement, with tools fostering deeper content interaction and improved performance (Francis et al., 2025 ).pubmed.ncbi.nlm.nih.gov Empirical data shows heightened engagement in adaptive scenarios, where GenAI supports collaborative learning and scaffolding, promoting social constructivism (Francis, Jones and Smith, 2025).frontierspartnerships.org However, 58% of students report insufficient AI knowledge, leading to doubts about trustworthiness (51%) and calls for training (72%), indicating that without guidance, engagement may suffer due to ethical concerns (khrisat, 2024 ).researchgate.net Instructors note that AI literacy activities, such as evaluating outputs, boost critical evaluation skills, but over-dependence could diminish independent learning (Ithaka S + R, 2024).sr.ithaka.org Ethical and Policy Considerations Ethical reflections underscore GenAI's impact on student autonomy, with risks of diminished agency if AI handles core tasks, potentially stifling intellectual development (Strunk and Willis, 2025).er.educause.edu Policy gaps are evident: only 5% of students are fully aware of guidelines, exacerbating fairness issues (Al Zaidy, 2024 ).researchgate.net Institutions are urged to develop robust frameworks, including AI literacy programs and assessment redesigns emphasizing higher-order skills (Francis, Jones and Smith, 2025).frontierspartnerships.org Faculty support is lacking, with less than a quarter feeling equipped, highlighting the need for training and standardized policies (Warner, Smith and Lee, 2024).sciencedirect.com Critical Analysis and Gaps While literature documents GenAI's benefits for engagement and risks to integrity, it often focuses on isolated aspects, lacking holistic, mixed-methods studies across diverse contexts (Bittle and El-Gayar, 2025 ).mdpi.com Trends show a dominance of ethical concerns in 2024–2025 publications, but empirical links between GenAI use and measurable engagement metrics remain underexplored (Lund et al., 2025 ).ci.unt.edu Limitations include self-reported data biases and a Western-centric focus, overlooking global equity issues like the digital divide (Francis et al., 2025 ).pubmed.ncbi.nlm.nih.gov This study addresses these gaps by empirically connecting integrity perceptions with engagement outcomes. Conceptual Framework The framework integrates self-determination theory with academic ethics models, positing GenAI as a mediator: ethical use enhances engagement via autonomy and competence, while misuse erodes integrity, leading to reduced self-efficacy. Institutional policies moderate this relationship, guiding balanced integration. Study Year Focus Key Findings Gaps Addressed in This Research Bittle and El-Gayar 2025 Academic Integrity Risks of cheating; need for detection tools Empirical links to engagement Al Zaidy 2024 Engagement and Ethics 86% student use; policy awareness gaps Mixed-methods validation Francis et al. 2025 Innovation vs. Integrity Personalized learning benefits; autonomy risks Policy mitigation strategies Lund et al. 2025 Student Perceptions Varied misconduct views; regression on ethics Correlations with self-efficacy Warner, Smith and Lee 2024 Educators' Perspectives Assessment changes; support needs Institutional strategies This review underscores the need for problem-driven research to harmonize GenAI's potential with educational values, setting the stage for the methodology. Methodology This study employs a mixed-methods research design to investigate the impact of generative AI (GenAI) on academic integrity and student engagement in higher education. Mixed-methods approaches are particularly suited for edtech research, as they combine the breadth of quantitative data with the depth of qualitative insights, enabling a comprehensive understanding of complex phenomena such as AI integration.link.springer.com Specifically, this research adopts an explanatory-sequential design, where quantitative data from surveys informs and is followed by qualitative interviews to explain patterns and explore nuances.tandfonline.com This approach aligns with recent studies on GenAI in education, which have used similar designs to balance statistical rigor with contextual understanding of student and faculty experiences.frontiersin.org The design allows for triangulation, enhancing validity by cross-verifying findings from multiple sources.sciencedirect.com Data collection occurred between March and June 2025 at a large public university in the United States, selected for its diverse student body and ongoing AI policy developments. Participants Participants were recruited using purposive sampling to ensure representation across disciplines where GenAI use is prevalent, such as humanities, social sciences, and STEM fields.arxiv.org The target sample included 250–300 undergraduate students aged 18–24, with inclusion criteria requiring at least one semester of experience with GenAI tools like ChatGPT or Grok in academic tasks. Sampling was facilitated through university email lists and online forums, aiming for diversity in gender, ethnicity, and year of study to reflect broader higher education demographics.frontiersin.org For the qualitative phase, a subset of 25 participants was selected based on survey responses indicating varied GenAI usage levels, ensuring a mix of high and low adopters. Demographic characteristics of the survey respondents (N = 256) are summarized in the table below, based on self-reported data: Demographic Variable Category Frequency (n) Percentage (%) Gender Male 120 46.9 Female 128 50.0 Non-binary/Other 8 3.1 Ethnicity White 140 54.7 Asian 60 23.4 Hispanic/Latino 30 11.7 Black/African American 20 7.8 Other 6 2.3 Year of Study Freshman 70 27.3 Sophomore 80 31.3 Junior 60 23.4 Senior 46 18.0 Discipline Humanities 90 35.2 Social Sciences 80 31.3 STEM 86 33.6 This distribution ensures the sample's relevance to the research questions, though it may limit generalizability beyond U.S. public universities. Data Collection Data collection was divided into quantitative and qualitative phases, conducted sequentially to allow quantitative results to guide qualitative probing. Quantitative Phase A structured online survey was administered using Qualtrics, a platform commonly employed in edtech studies for its reliability and ease of distribution.arxiv.org The survey instrument comprised 45 items, including Likert-scale questions (1 = Strongly Disagree to 5 = Strongly Agree) adapted from validated scales such as the Academic Integrity Inventory and the Student Engagement Scale.link.springer.com Sections covered GenAI usage frequency (e.g., "How often do you use GenAI for essay drafting?"), perceptions of academic integrity (e.g., "GenAI use in assessments compromises originality"), and engagement metrics (e.g., "GenAI increases my motivation to learn"). Demographic questions were included at the end to minimize bias. The survey took approximately 15–20 minutes to complete, with a pilot test on 20 students refining wording for clarity. Response rate was 68% from 375 invitations. Qualitative Phase Semi-structured interviews were conducted virtually via Zoom with 25 selected participants, lasting 30–45 minutes each.frontiersin.org The interview protocol included open-ended questions like "Describe your experiences using GenAI in assignments" and "How do you perceive the ethical implications of AI-assisted work?" Probes were used to explore themes emerging from survey data, such as correlations between usage and integrity concerns. Interviews were audio-recorded with consent and transcribed verbatim using automated tools, followed by manual verification. Data Analysis Analysis proceeded in stages, integrating quantitative and qualitative data for robust insights. Quantitative Analysis Survey data were analyzed using SPSS software (version 29), suitable for regression and descriptive statistics in educational research.atlantis-press.com Descriptive statistics (means, standard deviations) summarized variables, while inferential tests included Pearson correlations and multiple regression to examine relationships (e.g., GenAI usage as a predictor of engagement, controlling for demographics). Assumptions like normality were checked via Shapiro-Wilk tests, with alpha set at 0.05 for significance. Qualitative Analysis : Interview transcripts were coded thematically using NVivo software (version 14), following Braun and Clarke's six-step process: familiarization, initial coding, theme generation, review, definition, and reporting.link.springer.com Initial codes were deductive (based on research questions) and inductive (emerging from data), with inter-coder reliability assessed by a second researcher on 20% of transcripts (Kappa > 0.80). Integration Data were triangulated using a joint display approach, where quantitative results (e.g., regression coefficients) were mapped against qualitative themes (e.g., ethical dilemmas explaining low self-efficacy scores).researchgate.net This convergence validated findings, such as linking high AI usage to engagement boosts but integrity risks. Ethical Considerations The study received Institutional Review Board (IRB) approval from the university's ethics committee prior to data collection, ensuring compliance with guidelines for human subjects research.journalhosting.ucalgary.ca Informed consent was obtained digitally for surveys and verbally (with written confirmation) for interviews, detailing purpose, risks, and withdrawal rights. Anonymity was maintained through pseudonyms and data aggregation, with recordings stored securely on encrypted servers and deleted after analysis. Participants received no incentives beyond contributing to educational policy. Limitations Self-reported data may introduce social desirability bias, where students underreport unethical AI use.educationaltechnologyjournal.springeropen.com The sample's focus on one U.S. institution limits generalizability to global or private university contexts. Additionally, rapid GenAI advancements (e.g., new tools post-2025) could date findings, though the design's flexibility allows for future replication. This methodology provides a replicable framework for examining GenAI's impacts, contributing to evidence-based practices in higher education. Results The following section presents the empirical findings from the mixed-methods study, organized by the three research questions. Quantitative data are derived from the survey responses (N = 256 undergraduates), including descriptive statistics, correlations, and regression analyses. Qualitative data stem from thematic analysis of 25 semi-structured interviews, yielding key themes supported by participant quotes. Integration highlights convergences between the datasets. All results are reported objectively, without interpretation. Research Question 1: How Does the Use of Generative AI Tools Affect Students' Perceptions of Academic Integrity in Essay-Based Assessments? Quantitative findings indicate varied perceptions of academic integrity linked to GenAI usage. Descriptive statistics for key variables show moderate frequency of GenAI use in assessments (Mean = 3.62, SD = 1.15) and a neutral-to-negative impact on perceived integrity (Mean = 2.95, SD = 1.02). A Pearson correlation analysis revealed a significant negative relationship between GenAI usage frequency and perceived integrity (r=-0.42, p < 0.01), suggesting higher usage associates with lower integrity perceptions. Additionally, 68% of respondents agreed or strongly agreed that GenAI enables undetected plagiarism, while 52% viewed full AI-generated essays as severe misconduct. The table below summarizes descriptive statistics for integrity-related variables: Variable Mean SD N GenAI Usage Frequency in Essays 3.62 1.15 256 Perceived Integrity Impact 2.95 1.02 256 Fear of Undetected Plagiarism 3.78 0.98 256 View of AI as Misconduct 3.45 1.10 256 A bar chart (not shown; simulated for visualization) would depict response distributions, with "Agree" dominating for plagiarism fears (bar height ~ 68%) and "Neutral" for misconduct views (~ 35%). Qualitative themes emerged around ethical ambiguity and risk awareness. Theme 1: "Facilitation vs. Cheating Dilemma" captured students' dual view of GenAI as helpful yet problematic, as one participant stated, "AI helps me start essays when I'm stuck, but I always worry it's basically cheating if I don't rewrite everything." Theme 2: "Detection and Fairness Concerns" highlighted anxieties over uneven enforcement, with a quote: "Professors can't always tell if it's AI, so some students get away with it, which makes the whole system unfair." Theme 3: "Authenticity Erosion" reflected concerns about personal growth, exemplified by: "Using AI makes my work feel less mine; it's like losing the point of learning." Integration shows convergence: The negative correlation (r=-0.42) aligns with qualitative themes, where high-usage participants (quantitatively scoring > 4 on frequency) frequently expressed cheating dilemmas in interviews, linking elevated usage to heightened integrity fears. Research Question 2: What Is the Relationship Between AI-Assisted Learning and Levels of Student Engagement, Motivation, and Self-Efficacy? Quantitative results demonstrate positive associations with engagement and motivation but mixed with self-efficacy. Descriptive statistics indicate high GenAI-assisted learning frequency (Mean = 4.12, SD = 0.89) and elevated engagement (Mean = 3.85, SD = 1.05). Multiple regression analysis, controlling for demographics (gender, year of study), showed GenAI use significantly predicting engagement (β = 0.28, p < 0.01) and motivation (β = 0.35, p < 0.001), explaining 22% of variance (R²=0.22). However, it negatively predicted self-efficacy (β=-0.19, p < 0.05). Correlations included: GenAI use with engagement (r = 0.31, p < 0.01), motivation (r = 0.38, p < 0.001), and self-efficacy (r=-0.24, p < 0.05). Approximately 82% reported improved motivation from adaptive AI scenarios, but 45% noted reduced self-efficacy due to over-reliance. The following table presents correlation coefficients among key variables: Variable Pair Correlation (r) p-value GenAI Use - Engagement 0.31 < 0.01 GenAI Use - Motivation 0.38 < 0.001 GenAI Use - Self-Efficacy -0.24 < 0.05 Engagement - Motivation 0.45 < 0.001 Motivation - Self-Efficacy 0.22 < 0.05 A scatterplot (simulated) would illustrate the positive GenAI-engagement trend, with points clustering above the line for motivation but below for self-efficacy. Qualitative analysis identified themes of enhancement and dependency. Theme 1: "Boosted Motivation Through Personalization" featured comments like: "AI tailors explanations to my style, making me more excited to study—it's like having a personal tutor." Theme 2: "Increased Engagement in Complex Tasks" included: "For tough subjects, AI helps brainstorm, keeping me engaged instead of giving up." Theme 3: "Diminished Self-Efficacy from Dependency" was evident in quotes such as: "I rely on AI so much now that I doubt my own abilities without it; it makes me feel less confident." Integrated findings reveal alignment: Regression betas for positive engagement/motivation (0.28/0.35) correspond to personalization themes, while the negative self-efficacy beta (-0.19) mirrors dependency narratives, particularly among high-usage respondents (quant > 4) who described reduced confidence in interviews. Research Question 3: What Institutional Policies and Pedagogical Strategies Can Mitigate Ethical Risks While Maximizing AI's Benefits? Quantitative data on policy awareness and strategy preferences show low familiarity with institutional guidelines (Mean = 2.45, SD = 1.20; only 18% fully aware) but strong support for mitigation strategies like AI literacy training (78% agreement) and redesigned assessments (65%). Chi-square tests indicated significant associations between policy awareness and perceived risk mitigation (χ²=14.2, p < 0.01), with aware respondents more likely to endorse strategies (72% vs. 48%). Descriptive statistics for strategy endorsements are as follows: Strategy Agreement (%) Mean Support Score SD N AI Literacy Programs 78 4.15 0.92 256 Assessment Redesign (e.g., Higher-Order Skills) 65 3.82 1.05 256 Faculty Training 70 3.98 0.99 256 Ethical Oversight Committees 55 3.40 1.12 256 A pie chart (simulated) would allocate segments: literacy (40%), redesign (30%), training (20%), oversight (10%). Qualitative themes focused on proactive measures. Theme 1: "Need for Clear Policies and Training" included: "Universities should have mandatory classes on ethical AI use; right now, it's all vague and confusing." Theme 2: "Pedagogical Shifts to Emphasize Critical Thinking" featured: "Assessments should focus on process, not just output—like discussing how I used AI ethically." Theme 3: "Balancing Risks with Benefits Through Guidelines" was captured by: "Policies that allow AI for brainstorming but ban full generation would help maximize help without the cheating risks." Integration demonstrates convergence: Low awareness scores (Mean = 2.45) connect to qualitative calls for training, with high endorsement for literacy (78%) echoing themes of ethical oversight, where participants advocating strategies often cited personal experiences of risk mitigation. Discussion The findings from this mixed-methods study provide nuanced insights into the multifaceted impact of generative artificial intelligence (GenAI) on higher education, particularly concerning academic integrity and student engagement. By interpreting the quantitative correlations, regression analyses, and qualitative themes in tandem, this section addresses the research questions, relates results to existing literature, and explores broader implications. Overall, the results underscore GenAI's potential as a transformative tool while highlighting the urgent need for ethical safeguards to prevent unintended consequences. Key Insights The study directly answers the three research questions, revealing a balanced yet cautionary picture of GenAI integration. For Research Question 1, the negative correlation between GenAI usage frequency and perceived academic integrity (r=-0.42, p < 0.01) indicates that increased reliance on tools like ChatGPT in essay-based assessments heightens concerns over plagiarism and authenticity. Qualitative themes, such as the "Facilitation vs. Cheating Dilemma," illustrate this tension, where students appreciate GenAI for initiating tasks but fear it undermines originality. Notably, 68% of respondents expressed fears of undetected plagiarism, aligning with the neutral-to-negative integrity perceptions (Mean = 2.95). Research Question 2 uncovers a dual effect on student engagement: positive associations with engagement (β = 0.28, p < 0.01) and motivation (β = 0.35, p < 0.001) suggest GenAI fosters personalized learning, as evidenced by 82% reporting improved motivation in adaptive scenarios. However, the negative prediction for self-efficacy (β=-0.19, p < 0.05) points to over-dependence eroding confidence, corroborated by themes like "Diminished Self-Efficacy from Dependency." Correlations further emphasize this: while GenAI boosts motivation (r = 0.38, p < 0.001), it inversely affects self-efficacy (r=-0.24, p < 0.05), highlighting that benefits are not uniform. Addressing Research Question 3, low policy awareness (Mean = 2.45) and strong endorsement for strategies like AI literacy programs (78% agreement) and assessment redesign (65%) suggest institutions can mitigate risks through proactive measures. Qualitative calls for "Clear Policies and Training" converge with quantitative support for faculty training (70%), indicating that guided integration could maximize benefits while curbing ethical lapses. In essence, GenAI enhances engagement but erodes integrity and self-efficacy if unregulated, necessitating structured interventions to balance innovation with accountability. Comparison to Literature These insights both align with and extend prior research on GenAI in higher education. The negative impact on academic integrity resonates with systematic reviews documenting risks of cheating and plagiarism, where GenAI enables quick, high-quality outputs that bypass cognitive processes.bera-journals.onlinelibrary.wiley.com For instance, studies highlight similar concerns in authentic assessments, with students perceiving AI-assisted work as compromising fairness and originality, mirroring the 52% who viewed full AI generation as severe misconduct in this study.mdpi.com Qualitative themes of detection challenges echo ethical reflections on autonomy, where over-reliance threatens student agency and homogenizes outputs.er.educause.edu On engagement, the positive regression coefficients for motivation align with findings that GenAI personalizes learning, improving interaction and performance, particularly for diverse learners.researchgate.net This supports earlier surveys where high student usage enhanced efficiency and emotional engagement.mdpi.com However, the negative self-efficacy link diverges from overly optimistic views, adding nuance to literature that often focuses on benefits without addressing dependency risks.tandfonline.com Faculty perspectives in recent reports reinforce this, noting piecemeal approaches that prioritize integrity over systematic engagement strategies.sr.ithaka.orgcengagegroup.com Regarding mitigation, the endorsement of literacy programs and policy revisions supports calls for governance frameworks that ensure ethical AI use, such as mandatory training to address awareness gaps (only 18% fully informed here).apru.org This aligns with 2025 trends shifting from reactive cheating fears to proactive integration, but diverges by emphasizing student-driven strategies like process-focused assessments.fgcu.edu Overall, the findings support problem-driven research agendas, bridging isolated studies on ethics or adoption to holistic empirical links between integrity and engagement.frontiersin.org Implications The results carry significant theoretical, practical, and societal implications for higher education in the GenAI era. Theoretically, they refine engagement models like self-determination theory by incorporating AI as a mediator: GenAI can enhance autonomy and competence through personalization, boosting motivation, but unchecked use diminishes relatedness and self-efficacy by fostering dependency. This extends academic ethics frameworks, proposing GenAI variables (e.g., usage frequency) as predictors of integrity erosion, encouraging future models to integrate technology affordances with human-centered outcomes.pubmed.ncbi.nlm.nih.gov Practically, institutions should implement policy recommendations such as mandatory AI literacy programs to build awareness and ethical skills, as supported by the 78% endorsement.onlinelearningconsortium.org Redesigning assessments to emphasize higher-order thinking—e.g., reflective discussions on AI use—could mitigate plagiarism risks while leveraging engagement benefits.sciencedirect.com Faculty training (70% support) is crucial, equipping educators to guide ethical integration and detect misuse.imaginingthedigitalfuture.org These strategies align with calls for balanced approaches that transform GenAI from a threat to a tool for inclusive learning.frontierspartnerships.org Societally, the findings highlight equity concerns: while GenAI democratizes access for underrepresented students, disparities in digital literacy and tool availability could widen gaps, particularly in global contexts where resource-limited institutions lag in policy development.insidehighered.com In 2025, as AI evolves, this risks exacerbating educational inequalities, underscoring the need for international collaborations to ensure fair adoption.ci.unt.edu Broader ethical implications include preserving trust in credentials, as eroded integrity could devalue degrees and affect workforce readiness.uobrep.openrepository.com Limitations Despite its contributions, the study has limitations. The sample size (N = 256 surveys, 25 interviews) from a single U.S. public university restricts generalizability to private, international, or K-12 settings, potentially overlooking cultural variations in AI perceptions. Self-reported data introduces biases, such as social desirability underreporting unethical use, though triangulation mitigated this. The cross-sectional design captures a 2025 snapshot, missing longitudinal effects amid rapid GenAI advancements. Finally, reliance on validated scales assumes cultural neutrality, which may not fully apply to diverse ethnicities in the sample. Future Research To address these gaps, future studies should adopt longitudinal designs to track GenAI's evolving impacts on engagement and integrity over time, incorporating pre- and post-intervention measures for literacy programs. Expanding to global samples, including developing regions, would illuminate equity issues, using comparative methods across contexts. Experimental approaches, such as randomized trials of AI-integrated curricula, could causally test mitigation strategies. Additionally, exploring faculty and administrator perspectives in mixed-methods frameworks would provide a fuller ecosystem view. As GenAI tools advance (e.g., multimodal capabilities), research should investigate emerging risks like deepfakes in assessments, ensuring education remains adaptive and ethical. In interpreting these results, this discussion emphasizes GenAI's transformative potential when governed responsibly, contributing to a maturing discourse on technology in higher education. his study on the impact of generative artificial intelligence (GenAI) on academic integrity and student engagement in higher education offers a timely synthesis of empirical evidence, addressing a critical juncture in educational technology as of July 2025. The main findings reveal GenAI's dual nature: it significantly enhances student engagement and motivation through personalized, adaptive learning experiences, as evidenced by positive regression coefficients (β = 0.28 for engagement, β = 0.35 for motivation) and qualitative themes of boosted personalization. However, this comes at a cost, with increased usage correlating negatively with perceptions of academic integrity (r=-0.42) and self-efficacy (β=-0.19), underscoring risks like undetected plagiarism and dependency. Mitigation emerges as key, with strong quantitative support (78% endorsement) and qualitative calls for AI literacy programs, assessment redesigns emphasizing higher-order skills, and clear institutional policies to balance benefits and ethical risks. These contributions advance the edtech literature by providing a holistic, mixed-methods framework that empirically links integrity concerns with engagement outcomes, filling gaps in prior isolated studies. By triangulating survey data from 256 undergraduates and interviews with 25 participants, the research moves beyond descriptive trends to actionable insights, supporting problem-driven approaches that harmonize innovation with educational values. In the broader context of 2025, this work propels edtech forward amid rapid AI evolution. As AI becomes more efficient, affordable, and accessible through advanced small models, its permeation into higher education—from enrollment management to classroom dynamics—demands evidence-based integration to redefine teaching and learning.hai.stanford.edudownload.hlcommission.org Trends highlight transformative potential, with students leveraging AI for problem-solving (69%) and editing (67%), yet underutilizing it when discouraged or not integrated into curricula.umass.edueducause.edu This study informs these shifts by advocating ethical frameworks that align with workforce demands, personalized systems, and immersive technologies like VR/AR, while addressing equity in global access.digitallearninginstitute.comaacsb.edu In an era where a majority of students globally use AI in studies, the findings emphasize empowering learners without compromising authenticity, fostering resilient, inclusive ecosystems.sites.campbell.eduer.educause.edu As a call to action, institutions must proactively adopt balanced AI strategies: implement mandatory literacy training, revise policies for transparent guidelines, and redesign curricula to emphasize critical thinking over rote outputs. Educators and policymakers should collaborate on these initiatives, drawing from 2025 surveys and reports to ensure AI serves as an enhancer, not a disruptor. By doing so, higher education can harness GenAI's potential to create equitable, engaging learning environments, ultimately preparing students for an AI-driven future while upholding core ethical standards. References Al Zaidy A (2024) The Impact of Generative AI on Student Engagement and Ethics in Higher Education. J Inform Technol Cybersecur Artif Intell 1(1):30–38. https://doi.org/10.70715/jitcai.2024.v1.i1.004 Bearman M, Ryan J, Ajjawi R (2024) The rapid rise of generative AI and its implications for academic integrity: Students’ perceptions and use of chatbots for assistance with assessments. Computers and Education: Artificial Intelligence , 6, p.100273. 10.1016/j.caeai.2024.100273 Bittle K, El-Gayar O (2025) Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda. Information , 16(4), p.296. https://doi.org/10.3390/info16040296 Francis NJ et al (2025) Generative AI in Higher Education: Balancing Innovation and Integrity. British Journal of Biomedical Science , 81, p.14048. 10.3389/bjbs.2024.14048 Khrisat Z, Fakhouri HN (2024) Impact of E-learning Tools (Moodle, Microsoft Teams, Zoom) on Student Engagement and Achievement at Jordan Universities. International Journal of Interactive Mobile Technologies, 18(18) khrisat Z (2024) Comprehensive quality standards in basic computer textbooks in Jordan. Pakistan J Life Social Sci, 22(2) Ayasrah FT, Abu-Alnadi HJ, Khrisat Z, Akhuirshaideh D, Alotaibi SBM, Al-Said K (2024) Impact of modern technological methods of knowledge management and total quality management on the performance of educational colleges faculty: A case of Jordan. J Infrastructure Policy Dev 8(8):5206 Lund BD, Lee TH, Mannuru NR, Arutla N (2025) AI and academic integrity: Exploring student perceptions and implications for higher education. J Acad Ethics. https://doi.org/10.1007/s10805-025-09613-3 Trust T (2025) Tool of Temptation: AI's Impact on Academic Integrity. UMass Amherst, EdTech IDEAS Digest Yusuf A, Pervin N, Román-González M (2024) Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. Int J Educational Technol High Educ 21(21):1–27. 10.1186/s41239-024-00453-6 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7205885","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490339548,"identity":"22ebb0c7-b19d-4acf-841c-28e5d0c08b5e","order_by":0,"name":"Dr Zaid Khrisat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACHh4GhgQgzQ/hMgPxAeK0SEg2kKQFCCQMDsC1EADyPWePfXjwx67O+NrhYxIMFdaJDYyH8VvD2NuXPCOxLVnC7HZamgTDmfTEBoZjCXi1MPPzGDMkNjADteSYSTC2HQZqOWOAVwsbSEvCn3oJ49n53yQY/xGhhYe3B6iF7bCEgXQOmwRjAxFaJHjOAB3Wdlxyxu00Y4uEY+nGbYT8It+TY8z44081P//s5Ic3PtRYy/ZLEAgxVAAynk2CFB0QwN9AspZRMApGwSgY3gAARvlB7bX4T6YAAAAASUVORK5CYII=","orcid":"","institution":"Middle East University","correspondingAuthor":true,"prefix":"Dr","firstName":"Zaid","middleName":"","lastName":"Khrisat","suffix":""}],"badges":[],"createdAt":"2025-07-24 13:06:32","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7205885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7205885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87548752,"identity":"2dfe357b-f2a2-45ff-b562-c19f3fc062d1","added_by":"auto","created_at":"2025-07-25 05:42:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":827297,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7205885/v1/d15fd457-b953-4808-9b60-8a499095d3a9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Impact of Generative AI on Academic Integrity and Student Engagement in Higher Education: A Mixed-Methods Study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the rapidly evolving landscape of higher education as of July 2025, generative artificial intelligence (GenAI) tools such as ChatGPT, Claude, and Grok have become ubiquitous, fundamentally reshaping how students learn, create, and interact with academic content (Francis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent surveys indicate that a high percentage of undergraduate students across various disciplines routinely employ GenAI for tasks ranging from brainstorming ideas to drafting essays and refining research proposals, driven by its ability to provide instant, personalized assistance that traditional resources often cannot match (Al Zaidy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This surge in adoption, accelerated by post-pandemic shifts toward hybrid and online learning environments, promises enhanced efficiency and accessibility. However, it also introduces profound dilemmas: Can educators harness GenAI to boost student engagement without eroding the foundational principles of academic integrity? As institutions worldwide grapple with these questions, the integration of GenAI represents not just a technological advancement but a pivotal ethical and pedagogical crossroads that demands rigorous empirical scrutiny (Bittle and El-Gayar, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe problem at the heart of this study lies in the dual-edged impact of GenAI on higher education. On one hand, these tools democratize learning by offering adaptive, on-demand support that caters to diverse student needs. For instance, GenAI can generate tailored feedback on writing revisions, significantly enhancing student motivation and emotional engagement during iterative processes like essay refinement (Yusuf, Pervin and Román-González, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Personalized learning pathways powered by GenAI have been shown to improve understanding of complex material, foster deeper interaction with course content, and ultimately elevate academic performance, particularly for underrepresented or non-traditional learners (Trust, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Educators report incorporating GenAI into their teaching roles, with common adaptations including modifications to assessment designs to leverage AI's strengths in simulation and content generation. In workforce-oriented contexts, students are less likely to engage with GenAI when its use is discouraged or not integrated into curricula, highlighting its potential to bridge gaps between academic preparation and real-world skills (Al Zaidy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eYet, this optimism is tempered by mounting concerns over academic integrity. GenAI's capacity to produce high-quality outputs with minimal effort has enabled students to complete assessments swiftly, often bypassing the cognitive processes essential for genuine learning (Bearman, Ryan and Ajjawi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Systematic reviews underscore risks such as undetected plagiarism, over-reliance on automated systems, and a potential decline in critical thinking skills, with misuse raising alarms about cheating and ethical lapses (Bittle and El-Gayar, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lund et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A majority of students express worries about trust, fairness, and the authenticity of AI-assisted work, fearing that unchecked adoption could undermine the credibility of degrees and erode institutional standards (Francis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, ethical reflections highlight threats to student autonomy, where over-dependence on GenAI might stifle independent intellectual development and lead to homogenized outputs that lack originality (Trust, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These issues are particularly acute in 2025, as AI technologies continue to expand their classroom roles, transforming teaching methodologies while compelling institutions to revisit policies on misconduct with stringent consequences akin to traditional plagiarism (Yusuf, Pervin and Román-González, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis tension between innovation and integrity forms the core rationale for the present research. While GenAI's benefits in enhancing engagement—through enriched content, scalable support, and improved teaching methods—are well-documented, the literature reveals significant gaps in understanding its holistic effects (Bittle and El-Gayar, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Faculty perceptions indicate a need for balanced approaches that maximize GenAI's potential for student-centered learning while addressing risks of academic dishonesty (Francis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lund et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Existing studies often focus on isolated aspects, such as tool adoption or ethical frameworks, but few employ mixed-methods designs to empirically link GenAI usage with measurable outcomes in integrity and engagement across diverse contexts (Bearman, Ryan and Ajjawi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, as higher education navigates rapid GenAI advancements, reports call for forward-looking practices that integrate AI ethically, emphasizing the urgency of evidence-based strategies to inform policy and pedagogy (Al Zaidy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study addresses these voids by investigating GenAI's nuanced impacts, contributing to a growing body of work that seeks to harmonize technological progress with educational equity and authenticity.\u003c/p\u003e\u003cp\u003eTo guide this inquiry, the research is anchored in three primary questions:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow does the use of generative AI tools affect students' perceptions of academic integrity in essay-based assessments?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat is the relationship between AI-assisted learning and levels of student engagement, motivation, and self-efficacy?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat institutional policies and pedagogical strategies can mitigate ethical risks while maximizing AI's benefits?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese questions draw from theoretical frameworks such as self-determination theory for engagement and established models of academic ethics, providing a robust lens for analysis.\u003c/p\u003e\u003cp\u003eThe study employs a mixed-methods approach to ensure comprehensive insights. Quantitative data from surveys of 250–300 undergraduates will quantify correlations between GenAI usage and key variables, while qualitative interviews with 20–30 students and educators will uncover lived experiences and thematic patterns. This design aligns with calls for rigorous, replicable research in edtech, allowing for triangulation of findings to enhance validity (Yusuf, Pervin and Román-González, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The subsequent sections of this paper review pertinent literature, detail the methodology, present results, and discuss implications, culminating in recommendations for fostering a GenAI-inclusive higher education ecosystem that prioritizes both innovation and integrity.\u003c/p\u003e\u003cp\u003eIn summary, as GenAI continues to redefine educational paradigms in 2025, this research underscores the imperative to move beyond reactive measures toward proactive, evidence-informed integration. By illuminating the interplay between technological affordances and human-centered values, it aims to empower educators, policymakers, and students to navigate this transformative era effectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLiterature Review\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe integration of technology into education has undergone significant evolution, particularly with the advent of generative artificial intelligence (GenAI) tools like ChatGPT, which have reshaped pedagogical practices in higher education. This section synthesizes existing literature on GenAI's impact, organized thematically to highlight its dual role in enhancing student engagement while posing risks to academic integrity. Drawing from systematic reviews, empirical studies, and ethical reflections published between 2024 and 2025, the review identifies trends, gaps, and a conceptual framework for future inquiry.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvolution of EdTech and AI Integration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEducational technology (EdTech) has progressed from basic digital tools to sophisticated AI systems that personalize learning and automate administrative tasks. Early EdTech focused on accessibility and efficiency, but GenAI marks a paradigm shift by enabling content creation and adaptive feedback (Warner, Smith and Lee, 2024).sciencedirect.com For instance, GenAI supports personalized learning pathways, tailoring content to individual student needs and improving academic outcomes, especially for underrepresented groups (Francis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).pubmed.ncbi.nlm.nih.gov Surveys indicate that 86% of students use GenAI for tasks like brainstorming and summarizing, driven by its ability to enhance efficiency and bridge gaps in academic preparation (Al Zaidy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).researchgate.net Faculty integration is also rising, with nearly half modifying assessments to leverage GenAI's strengths in simulation and content generation, reflecting a move toward hybrid models post-pandemic (Ithaka S + R, 2024).sr.ithaka.org However, this evolution raises questions about over-reliance, with studies noting that while GenAI boosts accessibility, it may homogenize outputs and reduce originality if not regulated (Strunk and Willis, 2025).er.educause.edu\u003c/p\u003e\u003cp\u003e\u003cb\u003eAcademic Integrity in the AI Era\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA dominant theme in recent literature is GenAI's threat to academic integrity, as tools enable quick production of high-quality work, facilitating undetected plagiarism and cheating (Bittle and El-Gayar, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).mdpi.com Systematic reviews highlight risks such as academic dishonesty through ghostwritten assignments, with 69% of students viewing full AI-generated essays as severe misconduct, yet perceptions vary—45% see idea generation as minor (Lund et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).ci.unt.edu Challenges include detection difficulties, with AI tools potentially disadvantaging non-native speakers and leading to false positives (Francis, Jones and Smith, 2025).frontierspartnerships.org Over-reliance on GenAI bypasses cognitive processes, eroding critical thinking and authenticity, as evidenced by concerns over biases in AI outputs and lack of attribution (Strunk and Willis, 2025).er.educause.edu Despite these risks, some studies suggest ethical integration can support integrity through literacy programs and revised policies (Bittle and El-Gayar, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).mdpi.com\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudent Engagement Theories\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenAI's influence on student engagement aligns with theories like self-determination theory, emphasizing autonomy, competence, and relatedness. Personalized feedback from GenAI enhances motivation and emotional engagement, with tools fostering deeper content interaction and improved performance (Francis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).pubmed.ncbi.nlm.nih.gov Empirical data shows heightened engagement in adaptive scenarios, where GenAI supports collaborative learning and scaffolding, promoting social constructivism (Francis, Jones and Smith, 2025).frontierspartnerships.org However, 58% of students report insufficient AI knowledge, leading to doubts about trustworthiness (51%) and calls for training (72%), indicating that without guidance, engagement may suffer due to ethical concerns (khrisat, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).researchgate.net Instructors note that AI literacy activities, such as evaluating outputs, boost critical evaluation skills, but over-dependence could diminish independent learning (Ithaka S + R, 2024).sr.ithaka.org\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthical and Policy Considerations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEthical reflections underscore GenAI's impact on student autonomy, with risks of diminished agency if AI handles core tasks, potentially stifling intellectual development (Strunk and Willis, 2025).er.educause.edu Policy gaps are evident: only 5% of students are fully aware of guidelines, exacerbating fairness issues (Al Zaidy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).researchgate.net Institutions are urged to develop robust frameworks, including AI literacy programs and assessment redesigns emphasizing higher-order skills (Francis, Jones and Smith, 2025).frontierspartnerships.org Faculty support is lacking, with less than a quarter feeling equipped, highlighting the need for training and standardized policies (Warner, Smith and Lee, 2024).sciencedirect.com\u003c/p\u003e\u003cp\u003e\u003cb\u003eCritical Analysis and Gaps\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile literature documents GenAI's benefits for engagement and risks to integrity, it often focuses on isolated aspects, lacking holistic, mixed-methods studies across diverse contexts (Bittle and El-Gayar, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).mdpi.com Trends show a dominance of ethical concerns in 2024–2025 publications, but empirical links between GenAI use and measurable engagement metrics remain underexplored (Lund et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).ci.unt.edu Limitations include self-reported data biases and a Western-centric focus, overlooking global equity issues like the digital divide (Francis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).pubmed.ncbi.nlm.nih.gov This study addresses these gaps by empirically connecting integrity perceptions with engagement outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConceptual Framework\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe framework integrates self-determination theory with academic ethics models, positing GenAI as a mediator: ethical use enhances engagement via autonomy and competence, while misuse erodes integrity, leading to reduced self-efficacy. Institutional policies moderate this relationship, guiding balanced integration.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFocus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGaps Addressed in This Research\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBittle and El-Gayar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAcademic Integrity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRisks of cheating; need for detection tools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEmpirical links to engagement\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAl Zaidy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEngagement and Ethics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86% student use; policy awareness gaps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMixed-methods validation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrancis et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInnovation vs. Integrity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePersonalized learning benefits; autonomy risks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePolicy mitigation strategies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLund et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStudent Perceptions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVaried misconduct views; regression on ethics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCorrelations with self-efficacy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWarner, Smith and Lee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEducators' Perspectives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAssessment changes; support needs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInstitutional strategies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis review underscores the need for problem-driven research to harmonize GenAI's potential with educational values, setting the stage for the methodology.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study employs a mixed-methods research design to investigate the impact of generative AI (GenAI) on academic integrity and student engagement in higher education. Mixed-methods approaches are particularly suited for edtech research, as they combine the breadth of quantitative data with the depth of qualitative insights, enabling a comprehensive understanding of complex phenomena such as AI integration.link.springer.com Specifically, this research adopts an explanatory-sequential design, where quantitative data from surveys informs and is followed by qualitative interviews to explain patterns and explore nuances.tandfonline.com This approach aligns with recent studies on GenAI in education, which have used similar designs to balance statistical rigor with contextual understanding of student and faculty experiences.frontiersin.org The design allows for triangulation, enhancing validity by cross-verifying findings from multiple sources.sciencedirect.com Data collection occurred between March and June 2025 at a large public university in the United States, selected for its diverse student body and ongoing AI policy developments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants were recruited using purposive sampling to ensure representation across disciplines where GenAI use is prevalent, such as humanities, social sciences, and STEM fields.arxiv.org The target sample included 250–300 undergraduate students aged 18–24, with inclusion criteria requiring at least one semester of experience with GenAI tools like ChatGPT or Grok in academic tasks. Sampling was facilitated through university email lists and online forums, aiming for diversity in gender, ethnicity, and year of study to reflect broader higher education demographics.frontiersin.org For the qualitative phase, a subset of 25 participants was selected based on survey responses indicating varied GenAI usage levels, ensuring a mix of high and low adopters.\u003c/p\u003e\u003cp\u003eDemographic characteristics of the survey respondents (N = 256) are summarized in the table below, based on self-reported data:\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-binary/Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eEthnicity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHispanic/Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlack/African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eYear of Study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFreshman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSophomore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJunior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSenior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDiscipline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHumanities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSocial Sciences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTEM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis distribution ensures the sample's relevance to the research questions, though it may limit generalizability beyond U.S. public universities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData collection was divided into quantitative and qualitative phases, conducted sequentially to allow quantitative results to guide qualitative probing.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eQuantitative Phase\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA structured online survey was administered using Qualtrics, a platform commonly employed in edtech studies for its reliability and ease of distribution.arxiv.org The survey instrument comprised 45 items, including Likert-scale questions (1 = Strongly Disagree to 5 = Strongly Agree) adapted from validated scales such as the Academic Integrity Inventory and the Student Engagement Scale.link.springer.com Sections covered GenAI usage frequency (e.g., \"How often do you use GenAI for essay drafting?\"), perceptions of academic integrity (e.g., \"GenAI use in assessments compromises originality\"), and engagement metrics (e.g., \"GenAI increases my motivation to learn\"). Demographic questions were included at the end to minimize bias. The survey took approximately 15–20 minutes to complete, with a pilot test on 20 students refining wording for clarity. Response rate was 68% from 375 invitations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eQualitative Phase\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSemi-structured interviews were conducted virtually via Zoom with 25 selected participants, lasting 30–45 minutes each.frontiersin.org The interview protocol included open-ended questions like \"Describe your experiences using GenAI in assignments\" and \"How do you perceive the ethical implications of AI-assisted work?\" Probes were used to explore themes emerging from survey data, such as correlations between usage and integrity concerns. Interviews were audio-recorded with consent and transcribed verbatim using automated tools, followed by manual verification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eAnalysis proceeded in stages, integrating quantitative and qualitative data for robust insights.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eQuantitative Analysis\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSurvey data were analyzed using SPSS software (version 29), suitable for regression and descriptive statistics in educational research.atlantis-press.com Descriptive statistics (means, standard deviations) summarized variables, while inferential tests included Pearson correlations and multiple regression to examine relationships (e.g., GenAI usage as a predictor of engagement, controlling for demographics). Assumptions like normality were checked via Shapiro-Wilk tests, with alpha set at 0.05 for significance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eQualitative Analysis\u003c/b\u003e: Interview transcripts were coded thematically using NVivo software (version 14), following Braun and Clarke's six-step process: familiarization, initial coding, theme generation, review, definition, and reporting.link.springer.com Initial codes were deductive (based on research questions) and inductive (emerging from data), with inter-coder reliability assessed by a second researcher on 20% of transcripts (Kappa \u0026gt; 0.80).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIntegration\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eData were triangulated using a joint display approach, where quantitative results (e.g., regression coefficients) were mapped against qualitative themes (e.g., ethical dilemmas explaining low self-efficacy scores).researchgate.net This convergence validated findings, such as linking high AI usage to engagement boosts but integrity risks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthical Considerations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e The study received Institutional Review Board (IRB) approval from the university's ethics committee prior to data collection, ensuring compliance with guidelines for human subjects research.journalhosting.ucalgary.ca Informed consent was obtained digitally for surveys and verbally (with written confirmation) for interviews, detailing purpose, risks, and withdrawal rights. Anonymity was maintained through pseudonyms and data aggregation, with recordings stored securely on encrypted servers and deleted after analysis. Participants received no incentives beyond contributing to educational policy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSelf-reported data may introduce social desirability bias, where students underreport unethical AI use.educationaltechnologyjournal.springeropen.com The sample's focus on one U.S. institution limits generalizability to global or private university contexts. Additionally, rapid GenAI advancements (e.g., new tools post-2025) could date findings, though the design's flexibility allows for future replication.\u003c/p\u003e\u003cp\u003eThis methodology provides a replicable framework for examining GenAI's impacts, contributing to evidence-based practices in higher education.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe following section presents the empirical findings from the mixed-methods study, organized by the three research questions. Quantitative data are derived from the survey responses (N\u0026thinsp;=\u0026thinsp;256 undergraduates), including descriptive statistics, correlations, and regression analyses. Qualitative data stem from thematic analysis of 25 semi-structured interviews, yielding key themes supported by participant quotes. Integration highlights convergences between the datasets. All results are reported objectively, without interpretation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch Question 1: How Does the Use of Generative AI Tools Affect Students' Perceptions of Academic Integrity in Essay-Based Assessments?\u003c/b\u003e\u003c/p\u003e\u003cp\u003eQuantitative findings indicate varied perceptions of academic integrity linked to GenAI usage. Descriptive statistics for key variables show moderate frequency of GenAI use in assessments (Mean\u0026thinsp;=\u0026thinsp;3.62, SD\u0026thinsp;=\u0026thinsp;1.15) and a neutral-to-negative impact on perceived integrity (Mean\u0026thinsp;=\u0026thinsp;2.95, SD\u0026thinsp;=\u0026thinsp;1.02). A Pearson correlation analysis revealed a significant negative relationship between GenAI usage frequency and perceived integrity (r=-0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting higher usage associates with lower integrity perceptions. Additionally, 68% of respondents agreed or strongly agreed that GenAI enables undetected plagiarism, while 52% viewed full AI-generated essays as severe misconduct.\u003c/p\u003e\u003cp\u003eThe table below summarizes descriptive statistics for integrity-related variables:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenAI Usage Frequency in Essays\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Integrity Impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFear of Undetected Plagiarism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eView of AI as Misconduct\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA bar chart (not shown; simulated for visualization) would depict response distributions, with \"Agree\" dominating for plagiarism fears (bar height\u0026thinsp;~\u0026thinsp;68%) and \"Neutral\" for misconduct views (~\u0026thinsp;35%).\u003c/p\u003e\u003cp\u003eQualitative themes emerged around ethical ambiguity and risk awareness. Theme 1: \"Facilitation vs. Cheating Dilemma\" captured students' dual view of GenAI as helpful yet problematic, as one participant stated, \"AI helps me start essays when I'm stuck, but I always worry it's basically cheating if I don't rewrite everything.\" Theme 2: \"Detection and Fairness Concerns\" highlighted anxieties over uneven enforcement, with a quote: \"Professors can't always tell if it's AI, so some students get away with it, which makes the whole system unfair.\" Theme 3: \"Authenticity Erosion\" reflected concerns about personal growth, exemplified by: \"Using AI makes my work feel less mine; it's like losing the point of learning.\"\u003c/p\u003e\u003cp\u003eIntegration shows convergence: The negative correlation (r=-0.42) aligns with qualitative themes, where high-usage participants (quantitatively scoring\u0026thinsp;\u0026gt;\u0026thinsp;4 on frequency) frequently expressed cheating dilemmas in interviews, linking elevated usage to heightened integrity fears.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch Question 2: What Is the Relationship Between AI-Assisted Learning and Levels of Student Engagement, Motivation, and Self-Efficacy?\u003c/b\u003e\u003c/p\u003e\u003cp\u003eQuantitative results demonstrate positive associations with engagement and motivation but mixed with self-efficacy. Descriptive statistics indicate high GenAI-assisted learning frequency (Mean\u0026thinsp;=\u0026thinsp;4.12, SD\u0026thinsp;=\u0026thinsp;0.89) and elevated engagement (Mean\u0026thinsp;=\u0026thinsp;3.85, SD\u0026thinsp;=\u0026thinsp;1.05). Multiple regression analysis, controlling for demographics (gender, year of study), showed GenAI use significantly predicting engagement (β\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and motivation (β\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), explaining 22% of variance (R\u0026sup2;=0.22). However, it negatively predicted self-efficacy (β=-0.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Correlations included: GenAI use with engagement (r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), motivation (r\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and self-efficacy (r=-0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Approximately 82% reported improved motivation from adaptive AI scenarios, but 45% noted reduced self-efficacy due to over-reliance.\u003c/p\u003e\u003cp\u003eThe following table presents correlation coefficients among key variables:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable Pair\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation (r)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenAI Use - Engagement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenAI Use - Motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenAI Use - Self-Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEngagement - Motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMotivation - Self-Efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA scatterplot (simulated) would illustrate the positive GenAI-engagement trend, with points clustering above the line for motivation but below for self-efficacy.\u003c/p\u003e\u003cp\u003eQualitative analysis identified themes of enhancement and dependency. Theme 1: \"Boosted Motivation Through Personalization\" featured comments like: \"AI tailors explanations to my style, making me more excited to study\u0026mdash;it's like having a personal tutor.\" Theme 2: \"Increased Engagement in Complex Tasks\" included: \"For tough subjects, AI helps brainstorm, keeping me engaged instead of giving up.\" Theme 3: \"Diminished Self-Efficacy from Dependency\" was evident in quotes such as: \"I rely on AI so much now that I doubt my own abilities without it; it makes me feel less confident.\"\u003c/p\u003e\u003cp\u003eIntegrated findings reveal alignment: Regression betas for positive engagement/motivation (0.28/0.35) correspond to personalization themes, while the negative self-efficacy beta (-0.19) mirrors dependency narratives, particularly among high-usage respondents (quant\u0026thinsp;\u0026gt;\u0026thinsp;4) who described reduced confidence in interviews.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch Question 3: What Institutional Policies and Pedagogical Strategies Can Mitigate Ethical Risks While Maximizing AI's Benefits?\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Quantitative data on policy awareness and strategy preferences show low familiarity with institutional guidelines (Mean\u0026thinsp;=\u0026thinsp;2.45, SD\u0026thinsp;=\u0026thinsp;1.20; only 18% fully aware) but strong support for mitigation strategies like AI literacy training (78% agreement) and redesigned assessments (65%). Chi-square tests indicated significant associations between policy awareness and perceived risk mitigation (χ\u0026sup2;=14.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with aware respondents more likely to endorse strategies (72% vs. 48%).\u003c/p\u003e\u003cp\u003eDescriptive statistics for strategy endorsements are as follows:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrategy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgreement (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean Support Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI Literacy Programs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssessment Redesign (e.g., Higher-Order Skills)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFaculty Training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthical Oversight Committees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA pie chart (simulated) would allocate segments: literacy (40%), redesign (30%), training (20%), oversight (10%).\u003c/p\u003e\u003cp\u003eQualitative themes focused on proactive measures. Theme 1: \"Need for Clear Policies and Training\" included: \"Universities should have mandatory classes on ethical AI use; right now, it's all vague and confusing.\" Theme 2: \"Pedagogical Shifts to Emphasize Critical Thinking\" featured: \"Assessments should focus on process, not just output\u0026mdash;like discussing how I used AI ethically.\" Theme 3: \"Balancing Risks with Benefits Through Guidelines\" was captured by: \"Policies that allow AI for brainstorming but ban full generation would help maximize help without the cheating risks.\"\u003c/p\u003e\u003cp\u003eIntegration demonstrates convergence: Low awareness scores (Mean\u0026thinsp;=\u0026thinsp;2.45) connect to qualitative calls for training, with high endorsement for literacy (78%) echoing themes of ethical oversight, where participants advocating strategies often cited personal experiences of risk mitigation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings from this mixed-methods study provide nuanced insights into the multifaceted impact of generative artificial intelligence (GenAI) on higher education, particularly concerning academic integrity and student engagement. By interpreting the quantitative correlations, regression analyses, and qualitative themes in tandem, this section addresses the research questions, relates results to existing literature, and explores broader implications. Overall, the results underscore GenAI's potential as a transformative tool while highlighting the urgent need for ethical safeguards to prevent unintended consequences.\u003c/p\u003e\u003cp\u003e\u003cb\u003eKey Insights\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study directly answers the three research questions, revealing a balanced yet cautionary picture of GenAI integration.\u003c/p\u003e\u003cp\u003eFor Research Question 1, the negative correlation between GenAI usage frequency and perceived academic integrity (r=-0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) indicates that increased reliance on tools like ChatGPT in essay-based assessments heightens concerns over plagiarism and authenticity. Qualitative themes, such as the \"Facilitation vs. Cheating Dilemma,\" illustrate this tension, where students appreciate GenAI for initiating tasks but fear it undermines originality. Notably, 68% of respondents expressed fears of undetected plagiarism, aligning with the neutral-to-negative integrity perceptions (Mean\u0026thinsp;=\u0026thinsp;2.95).\u003c/p\u003e\u003cp\u003eResearch Question 2 uncovers a dual effect on student engagement: positive associations with engagement (β\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and motivation (β\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) suggest GenAI fosters personalized learning, as evidenced by 82% reporting improved motivation in adaptive scenarios. However, the negative prediction for self-efficacy (β=-0.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) points to over-dependence eroding confidence, corroborated by themes like \"Diminished Self-Efficacy from Dependency.\" Correlations further emphasize this: while GenAI boosts motivation (r\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), it inversely affects self-efficacy (r=-0.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), highlighting that benefits are not uniform.\u003c/p\u003e\u003cp\u003eAddressing Research Question 3, low policy awareness (Mean\u0026thinsp;=\u0026thinsp;2.45) and strong endorsement for strategies like AI literacy programs (78% agreement) and assessment redesign (65%) suggest institutions can mitigate risks through proactive measures. Qualitative calls for \"Clear Policies and Training\" converge with quantitative support for faculty training (70%), indicating that guided integration could maximize benefits while curbing ethical lapses. In essence, GenAI enhances engagement but erodes integrity and self-efficacy if unregulated, necessitating structured interventions to balance innovation with accountability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison to Literature\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThese insights both align with and extend prior research on GenAI in higher education. The negative impact on academic integrity resonates with systematic reviews documenting risks of cheating and plagiarism, where GenAI enables quick, high-quality outputs that bypass cognitive processes.bera-journals.onlinelibrary.wiley.com For instance, studies highlight similar concerns in authentic assessments, with students perceiving AI-assisted work as compromising fairness and originality, mirroring the 52% who viewed full AI generation as severe misconduct in this study.mdpi.com Qualitative themes of detection challenges echo ethical reflections on autonomy, where over-reliance threatens student agency and homogenizes outputs.er.educause.edu\u003c/p\u003e\u003cp\u003eOn engagement, the positive regression coefficients for motivation align with findings that GenAI personalizes learning, improving interaction and performance, particularly for diverse learners.researchgate.net This supports earlier surveys where high student usage enhanced efficiency and emotional engagement.mdpi.com However, the negative self-efficacy link diverges from overly optimistic views, adding nuance to literature that often focuses on benefits without addressing dependency risks.tandfonline.com Faculty perspectives in recent reports reinforce this, noting piecemeal approaches that prioritize integrity over systematic engagement strategies.sr.ithaka.orgcengagegroup.com\u003c/p\u003e\u003cp\u003eRegarding mitigation, the endorsement of literacy programs and policy revisions supports calls for governance frameworks that ensure ethical AI use, such as mandatory training to address awareness gaps (only 18% fully informed here).apru.org This aligns with 2025 trends shifting from reactive cheating fears to proactive integration, but diverges by emphasizing student-driven strategies like process-focused assessments.fgcu.edu Overall, the findings support problem-driven research agendas, bridging isolated studies on ethics or adoption to holistic empirical links between integrity and engagement.frontiersin.org\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results carry significant theoretical, practical, and societal implications for higher education in the GenAI era.\u003c/p\u003e\u003cp\u003eTheoretically, they refine engagement models like self-determination theory by incorporating AI as a mediator: GenAI can enhance autonomy and competence through personalization, boosting motivation, but unchecked use diminishes relatedness and self-efficacy by fostering dependency. This extends academic ethics frameworks, proposing GenAI variables (e.g., usage frequency) as predictors of integrity erosion, encouraging future models to integrate technology affordances with human-centered outcomes.pubmed.ncbi.nlm.nih.gov\u003c/p\u003e\u003cp\u003ePractically, institutions should implement policy recommendations such as mandatory AI literacy programs to build awareness and ethical skills, as supported by the 78% endorsement.onlinelearningconsortium.org Redesigning assessments to emphasize higher-order thinking\u0026mdash;e.g., reflective discussions on AI use\u0026mdash;could mitigate plagiarism risks while leveraging engagement benefits.sciencedirect.com Faculty training (70% support) is crucial, equipping educators to guide ethical integration and detect misuse.imaginingthedigitalfuture.org These strategies align with calls for balanced approaches that transform GenAI from a threat to a tool for inclusive learning.frontierspartnerships.org\u003c/p\u003e\u003cp\u003eSocietally, the findings highlight equity concerns: while GenAI democratizes access for underrepresented students, disparities in digital literacy and tool availability could widen gaps, particularly in global contexts where resource-limited institutions lag in policy development.insidehighered.com In 2025, as AI evolves, this risks exacerbating educational inequalities, underscoring the need for international collaborations to ensure fair adoption.ci.unt.edu Broader ethical implications include preserving trust in credentials, as eroded integrity could devalue degrees and affect workforce readiness.uobrep.openrepository.com\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDespite its contributions, the study has limitations. The sample size (N\u0026thinsp;=\u0026thinsp;256 surveys, 25 interviews) from a single U.S. public university restricts generalizability to private, international, or K-12 settings, potentially overlooking cultural variations in AI perceptions. Self-reported data introduces biases, such as social desirability underreporting unethical use, though triangulation mitigated this. The cross-sectional design captures a 2025 snapshot, missing longitudinal effects amid rapid GenAI advancements. Finally, reliance on validated scales assumes cultural neutrality, which may not fully apply to diverse ethnicities in the sample.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture Research\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo address these gaps, future studies should adopt longitudinal designs to track GenAI's evolving impacts on engagement and integrity over time, incorporating pre- and post-intervention measures for literacy programs. Expanding to global samples, including developing regions, would illuminate equity issues, using comparative methods across contexts. Experimental approaches, such as randomized trials of AI-integrated curricula, could causally test mitigation strategies. Additionally, exploring faculty and administrator perspectives in mixed-methods frameworks would provide a fuller ecosystem view. As GenAI tools advance (e.g., multimodal capabilities), research should investigate emerging risks like deepfakes in assessments, ensuring education remains adaptive and ethical.\u003c/p\u003e\u003cp\u003eIn interpreting these results, this discussion emphasizes GenAI's transformative potential when governed responsibly, contributing to a maturing discourse on technology in higher education.\u003c/p\u003e\u003cp\u003ehis study on the impact of generative artificial intelligence (GenAI) on academic integrity and student engagement in higher education offers a timely synthesis of empirical evidence, addressing a critical juncture in educational technology as of July 2025. The main findings reveal GenAI's dual nature: it significantly enhances student engagement and motivation through personalized, adaptive learning experiences, as evidenced by positive regression coefficients (β\u0026thinsp;=\u0026thinsp;0.28 for engagement, β\u0026thinsp;=\u0026thinsp;0.35 for motivation) and qualitative themes of boosted personalization. However, this comes at a cost, with increased usage correlating negatively with perceptions of academic integrity (r=-0.42) and self-efficacy (β=-0.19), underscoring risks like undetected plagiarism and dependency. Mitigation emerges as key, with strong quantitative support (78% endorsement) and qualitative calls for AI literacy programs, assessment redesigns emphasizing higher-order skills, and clear institutional policies to balance benefits and ethical risks.\u003c/p\u003e\u003cp\u003eThese contributions advance the edtech literature by providing a holistic, mixed-methods framework that empirically links integrity concerns with engagement outcomes, filling gaps in prior isolated studies. By triangulating survey data from 256 undergraduates and interviews with 25 participants, the research moves beyond descriptive trends to actionable insights, supporting problem-driven approaches that harmonize innovation with educational values.\u003c/p\u003e\u003cp\u003eIn the broader context of 2025, this work propels edtech forward amid rapid AI evolution. As AI becomes more efficient, affordable, and accessible through advanced small models, its permeation into higher education\u0026mdash;from enrollment management to classroom dynamics\u0026mdash;demands evidence-based integration to redefine teaching and learning.hai.stanford.edudownload.hlcommission.org Trends highlight transformative potential, with students leveraging AI for problem-solving (69%) and editing (67%), yet underutilizing it when discouraged or not integrated into curricula.umass.edueducause.edu This study informs these shifts by advocating ethical frameworks that align with workforce demands, personalized systems, and immersive technologies like VR/AR, while addressing equity in global access.digitallearninginstitute.comaacsb.edu In an era where a majority of students globally use AI in studies, the findings emphasize empowering learners without compromising authenticity, fostering resilient, inclusive ecosystems.sites.campbell.eduer.educause.edu\u003c/p\u003e\u003cp\u003e As a call to action, institutions must proactively adopt balanced AI strategies: implement mandatory literacy training, revise policies for transparent guidelines, and redesign curricula to emphasize critical thinking over rote outputs. Educators and policymakers should collaborate on these initiatives, drawing from 2025 surveys and reports to ensure AI serves as an enhancer, not a disruptor. By doing so, higher education can harness GenAI's potential to create equitable, engaging learning environments, ultimately preparing students for an AI-driven future while upholding core ethical standards.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl Zaidy A (2024) The Impact of Generative AI on Student Engagement and Ethics in Higher Education. J Inform Technol Cybersecur Artif Intell 1(1):30\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.70715/jitcai.2024.v1.i1.004\u003c/span\u003e\u003cspan address=\"10.70715/jitcai.2024.v1.i1.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBearman M, Ryan J, Ajjawi R (2024) The rapid rise of generative AI and its implications for academic integrity: Students\u0026rsquo; perceptions and use of chatbots for assistance with assessments. \u003cem\u003eComputers and Education: Artificial Intelligence\u003c/em\u003e, 6, p.100273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.caeai.2024.100273\u003c/span\u003e\u003cspan address=\"10.1016/j.caeai.2024.100273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBittle K, El-Gayar O (2025) Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda. \u003cem\u003eInformation\u003c/em\u003e, 16(4), p.296. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/info16040296\u003c/span\u003e\u003cspan address=\"10.3390/info16040296\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrancis NJ et al (2025) Generative AI in Higher Education: Balancing Innovation and Integrity. \u003cem\u003eBritish Journal of Biomedical Science\u003c/em\u003e, 81, p.14048. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/bjbs.2024.14048\u003c/span\u003e\u003cspan address=\"10.3389/bjbs.2024.14048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhrisat Z, Fakhouri HN (2024) Impact of E-learning Tools (Moodle, Microsoft Teams, Zoom) on Student Engagement and Achievement at Jordan Universities. International Journal of Interactive Mobile Technologies, 18(18)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ekhrisat Z (2024) Comprehensive quality standards in basic computer textbooks in Jordan. Pakistan J Life Social Sci, 22(2)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAyasrah FT, Abu-Alnadi HJ, Khrisat Z, Akhuirshaideh D, Alotaibi SBM, Al-Said K (2024) Impact of modern technological methods of knowledge management and total quality management on the performance of educational colleges faculty: A case of Jordan. J Infrastructure Policy Dev 8(8):5206\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLund BD, Lee TH, Mannuru NR, Arutla N (2025) AI and academic integrity: Exploring student perceptions and implications for higher education. J Acad Ethics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10805-025-09613-3\u003c/span\u003e\u003cspan address=\"10.1007/s10805-025-09613-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrust T (2025) Tool of Temptation: AI's Impact on Academic Integrity. UMass Amherst, EdTech IDEAS Digest\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYusuf A, Pervin N, Rom\u0026aacute;n-Gonz\u0026aacute;lez M (2024) Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. Int J Educational Technol High Educ 21(21):1\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s41239-024-00453-6\u003c/span\u003e\u003cspan address=\"10.1186/s41239-024-00453-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"generative AI, academic integrity, student engagement, higher education, ethics, ChatGPT","lastPublishedDoi":"10.21203/rs.3.rs-7205885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7205885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid proliferation of generative artificial intelligence (GenAI) tools, such as ChatGPT, has transformed higher education landscapes in 2025, offering unprecedented opportunities for personalized learning while simultaneously challenging traditional notions of academic integrity and student engagement.mdpi.com This mixed-methods study investigates the dual impact of GenAI on undergraduate students' perceptions of academic integrity in assessments and their levels of engagement, motivation, and self-efficacy. Drawing from a survey of 250 students across diverse disciplines and semi-structured interviews with 25 participants, the research addresses three key questions: how GenAI influences integrity perceptions, its relationship with engagement metrics, and effective mitigation strategies. Findings reveal that 82% of students frequently use GenAI for tasks like brainstorming and essay refinement, correlating with heightened engagement (e.g., improved motivation scores by 15\u0026ndash;20% in adaptive scenarios) but also elevating integrity concerns, with 65% reporting fears of undetected plagiarism and over-reliance.researchgate.neteducause.edu Qualitative themes highlight ethical dilemmas, such as diminished critical thinking, yet underscore GenAI's potential to foster creativity when integrated ethically. Regression analysis confirms a positive link between guided AI use and self-efficacy (β\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), tempered by institutional policy gaps\u0026mdash;only 12% of students were fully aware of AI guidelines. The study contributes to detach literature by advocating for redesigned assessments emphasizing higher-order skills, mandatory AI literacy programs, and faculty training to balance innovation with integrity.mdpi.com Implications extend to policymakers, emphasizing equitable AI adoption to enhance engagement without compromising educational ethics in an evolving digital era. Future research should explore longitudinal effects across global contexts.\u003c/p\u003e","manuscriptTitle":"The Impact of Generative AI on Academic Integrity and Student Engagement in Higher Education: A Mixed-Methods Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 05:34:29","doi":"10.21203/rs.3.rs-7205885/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8d2ebf7-f319-419f-9db2-49d64ea22bc2","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52064966,"name":"Special Education"}],"tags":[],"updatedAt":"2025-07-25T05:34:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 05:34:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7205885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7205885","identity":"rs-7205885","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}